Precision and Adaptability of YOLOv5 and YOLOv8 in Dynamic Robotic Environments
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914819204972544 |
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| author | Kich, Victor A. Muttaqien, Muhammad A. Toyama, Junya Miyoshi, Ryutaro Ida, Yosuke Ohya, Akihisa Date, Hisashi |
| author_facet | Kich, Victor A. Muttaqien, Muhammad A. Toyama, Junya Miyoshi, Ryutaro Ida, Yosuke Ohya, Akihisa Date, Hisashi |
| contents | Recent advancements in real-time object detection frameworks have spurred extensive research into their application in robotic systems. This study provides a comparative analysis of YOLOv5 and YOLOv8 models, challenging the prevailing assumption of the latter's superiority in performance metrics. Contrary to initial expectations, YOLOv5 models demonstrated comparable, and in some cases superior, precision in object detection tasks. Our analysis delves into the underlying factors contributing to these findings, examining aspects such as model architecture complexity, training dataset variances, and real-world applicability. Through rigorous testing and an ablation study, we present a nuanced understanding of each model's capabilities, offering insights into the selection and optimization of object detection frameworks for robotic applications. Implications of this research extend to the design of more efficient and contextually adaptive systems, emphasizing the necessity for a holistic approach to evaluating model performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_00315 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Precision and Adaptability of YOLOv5 and YOLOv8 in Dynamic Robotic Environments Kich, Victor A. Muttaqien, Muhammad A. Toyama, Junya Miyoshi, Ryutaro Ida, Yosuke Ohya, Akihisa Date, Hisashi Robotics Computer Vision and Pattern Recognition Recent advancements in real-time object detection frameworks have spurred extensive research into their application in robotic systems. This study provides a comparative analysis of YOLOv5 and YOLOv8 models, challenging the prevailing assumption of the latter's superiority in performance metrics. Contrary to initial expectations, YOLOv5 models demonstrated comparable, and in some cases superior, precision in object detection tasks. Our analysis delves into the underlying factors contributing to these findings, examining aspects such as model architecture complexity, training dataset variances, and real-world applicability. Through rigorous testing and an ablation study, we present a nuanced understanding of each model's capabilities, offering insights into the selection and optimization of object detection frameworks for robotic applications. Implications of this research extend to the design of more efficient and contextually adaptive systems, emphasizing the necessity for a holistic approach to evaluating model performance. |
| title | Precision and Adaptability of YOLOv5 and YOLOv8 in Dynamic Robotic Environments |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.00315 |